Protein Model Quality Assessment Based on Dynamic Graph Convolution and Transfer Learning
Protein model quality assessment refers to the scoring of protein models predicted by computational methods,so as to select an ex-cellent model that is closer to the native structure.Graph structures can intuitively represent protein models,so graph convolutional neural net-works(GCNs)have been widely used in quality assessment in recent years.However,the fixed adjacency relationship of graph nodes limits the ability of GCN to mine node features deeply.Based on this,a dynamic graph convolution quality assessment method DGCQA is proposed to predict the global quality score of the protein model.This method dynamically obtains the neighborhood according to the feature distance of the node,and combines the multi-scale convolution module to extract the residue pair features to enhance the expressive ability of the network.In addition,based on the idea of transfer learning,the protein pre-training model ESM-1b encoding feature is introduced,which improves the performance of DGCQA on multiple indicators.The final experiments show that DGCQA is highly competitive in comparison with 12 quality as-sessment methods based on the CASP13 dataset.
protein model quality assessmentdynamic graph convolutiontransfer learningESM-1b
国家科技期刊平台
NSTL
万方数据
维普
